Method and system for automating the creation of customer-centric interfaces

ABSTRACT

An interface is provided by creating prompts for the interface. The prompts represent tasks to be accomplished by a user and are obtained based on user input. The prompts are grouped according to relationships, obtained from the user input, among the tasks. The interface is updated based on user feedback. Each of the prompts is designated using user terminology obtained from the user input.

RELATED PATENT APPLICATION

This application is a continuation of pending U.S. patent applicationSer. No. 10/217,873, filed Aug. 13, 2002, which is acontinuation-in-part of U.S. patent application Ser. No. 09/532,038,filed Mar. 21, 2000, the disclosures of which are expressly incorporatedherein by reference in their entireties.

TECHNICAL FIELD OF THE INVENTION

The present invention relates generally to interface designs, and morespecifically relates to a system and method for automating the creationof customer-centric interfaces.

BACKGROUND OF THE INVENTION

Every year, company service centers typically receive numerous telephonecalls from customers seeking assistance with particular tasks. Thecustomers often speak with customer service representatives (CSR) tocomplete their tasks. Because of the cost associated with CSR time,companies are switching over to automated systems such as interactivevoice response (IVR) systems where IVR systems answer the customer phonecalls and direct the customer phone calls to the correct service centerusing one or more menus of options. The IVR systems allow customers tocomplete their tasks without the assistance of a CSR. In order tomaintain a high level of customer satisfaction, an IVR system must bedesigned so that customers can easily navigate the various menus andaccomplish their tasks without spending too much time on the telephoneand becoming frustrated and unsatisfied with the company and itscustomer service. Many IVR systems are designed around how companies areorganized which can make navigation of the IVR system difficult for thecustomers because although the customers know what task they want toaccomplish, the customers typically do not know which departments withina company organization manage which tasks.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present embodiments and advantagesthereof may be acquired by referring to the following description takenin conjunction with the accompanying drawings, in which like referencenumbers indicate like features, and wherein:

FIG. 1 depicts a block diagram of a system for the automated creation ofa customer-centric interface;

FIG. 2 depicts a flow diagram of a method for automating the creation ofa customer-centric interface;

FIG. 3 depicts an example task frequency table;

FIG. 4 illustrates a block flow diagram of various components of thesystem for automated creation of a customer-centric interface; and

FIG. 5 illustrates a flow diagram of a method for creatingcustomer-centric menu prompts.

DETAILED DESCRIPTION OF THE INVENTION

Preferred embodiments of the present invention are illustrated in thefigures, like numerals being used to refer to like and correspondingparts of the various drawings.

Many companies that have customer service programs and/or call centers,such as telephone companies, Internet service providers, and credit cardcompanies, typically have automated systems such as interactive voiceresponse (IVR) systems that answer and direct customer phone calls whena customer calls seeking assistance for a particular task such as tochange an address or inquire about payment of a bill. If a customer doesnot reach an IVR system when calling a service number, the customer mayspeak with a customer service representative (CSR) who either helps thecustomer or transfers the customer to an IVR. Within the IVR, thecustomer listens to one or more prerecorded menus or prompts andprovides responses using either touch-tone input or speech input inorder to accomplish their task. Therefore, the content and structure ofthe IVR including the prerecorded menus or prompts needs to allow forcustomers to easily and quickly accomplish their tasks with littlefrustration.

The typical approach to IVR system interface design involves a companydesign team creating a set of requirements where the design team iscomprised of various individuals representing different departmentswithin the company. The design team incorporates various perspectivesand documents from the team members in designing the IVR interface. Thedesign team decides how best to structure the interface based on theirunderstanding of the underlying system and the organization of thecompany and the customers' preferences and level of knowledge aregenerally not taken into account.

Once designed, the IVR interface is tested to ensure functionality andthat it is error free. The inclusion of customers into the designprocess occurs late in the development phase, if it all, through theusability testing. But much of the customer input gathered in theusability testing will not be implemented into the IVR interface becauseof the costs involved with making changes late in the development phaseand only significant errors discovered through the usability testing aregenerally corrected. The result is an IVR interface having abusiness-centric organization and structure where the menu options andprompts are structured according to the organization of the company andare worded using company terminology.

When calling a customer service number, customers know why they arecalling (to accomplish a specific task) but typically do not know whichdepartment within a company handles specific tasks. Therefore,business-centric interfaces generally do not allow for customers toeasily and quickly navigate and accomplish their tasks with littlefrustration since business-centric interfaces are designed around acompany's organization and way of thinking. When customers cannotquickly and easily accomplish their tasks, they generally make incorrectselections within the IVR interface resulting in misdirected calls.Misdirected calls are expensive to companies both in the time and moneyspent dealing with a misdirected call and in lower levels of customersatisfaction resulting from unpleasant customer experiences withbusiness-centric interfaces which can lead to negative feelings towardsthe company.

By contrast, the example embodiment described herein allows for theautomated creation of a customer-centric interface. The customer-centricinterface is designed to best represent the customers' preferences andlevels of knowledge and understanding. Additionally, the exampleembodiment allows for the inclusion of the customers in the designprocess from the beginning to ensure that the customer-centric interfaceis both usable and useful for the customers. The customer-centricinterface allows for the customers to quickly and easily navigate thevarious menus within the customer-centric interface to accomplish theirtasks with high levels of customer satisfaction. The customer-centricdesign also allows for increased call routing accuracy and a reductionin the number of misdirected calls. Therefore, companies save time andmoney because less time is spent dealing with misdirected calls and lessresources are used by the customers since the customers spend less timewithin the customer-centric interface accomplishing their tasks.

Referring now to FIG. 1, a block diagram depicts customer-centricinterface system 10 for automating the creation of customer-centricinterfaces. In the example embodiment, customer-centric interface system10 may include respective software components and hardware components,such as processor 12, memory 14, input/output ports 16, hard disk drive(HDD) 18 containing databases 20, 22, 24, 26, and 28 and thosecomponents may work together via bus 30 to provide the desiredfunctionality. The various hardware and software components may also bereferred to as processing resources. Customer-centric interface system10 may be a personal computer, a server, or any other appropriatecomputing device. Customer-centric interface system 10 may furtherinclude a display and input devices such as a mouse and a keyboard.Customer-centric interface system 10 also includes collection engine 32,customer language engine 34, task frequency engine 36, customerstructure engine 38, and customer performance engine 40, which reside inmemory such as HDD 18 and are executable by processor 12 through bus 30.In alternate embodiments, HDD 18 may include more or less than fivedatabases.

FIG. 2 depicts a flow diagram of a method for automating the creation ofa customer-centric interface. The method begins at step 50 and at step52 collection engine 32 collects a plurality of customer openingstatements. When a customer calls a service number and speaks to a CSR,the customer typically tells the CSR the purpose of the call in thefirst substantive statement the customer makes. Alternatively, acustomer may contact a company via the company web site or email andgenerally the first substantive statement made in the email or web siteresponse includes the customer's purpose for contacting the company.These initial statements containing the purpose of the customer's callare often referred to as customer opening statements. Collection engine32 collects the customer opening statements from customer servicecenters and stores the customer opening statement in customer openingstatement database 20.

The customer opening statements provide insight into the tasks that thecustomers inquire about as well as the language or terminology thecustomers use to describe the tasks. At step 54, customer languageengine 34 analyzes the customer opening statements to determine thelanguage or terminology used by the customers when referring toparticular tasks. When customers call a service number, they are notconcerned with how the company is going to accomplish the task just thatthe task gets accomplished. Therefore, customer language engine 34 mustlearn and use the terminology of the customers in creatingcustomer-centric menu prompts so that customers will be able to easilyunderstand and identify how to accomplish their tasks when using thecustomer-centric interface.

At step 56, customer task model 150 within collection engine 32determines the different reasons why the customers contact the companyin order to create a list of tasks for which the customers access thecustomer-centric interface. Analysis of the customer opening statementsallows for the determined tasks to be tested to see if the list of tasksaccounts for a majority of the reasons why the customer contact thecompany. The tasks may include such tasks as “telephone line is notworking,” “question about my bill,” “order a new service,” or any otherappropriate reason for a customer to call seeking assistance regarding aproduct or service.

Once the list of tasks has been created and determined to cover themajority of the customers' reasons for calling, task frequency engine 36determines a task frequency of occurrence for each task at step 58. Thetask frequency of occurrence allows customer-centric interface system 10to recognize which tasks customers are calling about the most and whichtasks the customers are calling about the least. Task frequency engine36 determines the task frequency of occurrence by examining andcategorizing the customer opening statements. Each customer openingstatement is examined to identify the purpose of the call and is thencategorized as a particular task.

Once the customer opening statements have been categorized, taskfrequency engine 36 creates a task frequency table that ranks the tasksaccording to the task frequency of occurrence. The task frequency tabledetails how often customers call with specific problems or questionsabout each particular task. An example task frequency table 100 foreighteen tasks 108-142 is shown in FIG. 3 and includes column 102 forthe frequency rank of the task, column 104 for the task, and column 106for the frequency value. In alternate embodiments, task frequency table100 may include more or less than eighteen tasks. Task frequency table100 shows that eighteen tasks account for more than 80% of the customeropening statements or service calls received from the customers. Taskfrequency table 100 allows for customer-centric interface system 10 todetermine which tasks the customers call about the most and providesvaluable information on how to arrange the customer-centric menu promptswithin the customer-centric interface.

Task frequency table 100 is ordered in descending frequency order and isa statistically valid representation of the tasks that the customersinquire about when calling customer service centers. Because having amenu prompt for every single task results in numerous menu promptsmaking customer navigation of the customer-centric interface burdensomeand slow, at step 60 task frequency engine 36 determines which tasks areto be included in the customer-centric interface. In order to allow easyand quick navigation for the customers but at the same time not utilizetoo many company resources operating the customer-centric interface,only the most frequently occurring tasks are included within thecustomer-centric interface.

Task frequency engine 36 utilizes task frequency table 100 to determinewhich tasks are to be included in the customer-centric interface. In oneembodiment, task frequency engine 36 includes only the tasks that have afrequency of occurrence of 1% or higher Task frequency table 100includes only the tasks having a frequency of occurrence of 1% or higherand includes eighteen tasks accounting for 80.20% of the tasksrepresented in the customer opening statement. In an alternateembodiment, task frequency engine 36 includes tasks so that the totalnumber of included tasks accounts for a specified percentage coverage ofthe tasks represented in the customer opening statements. For instance,task frequency engine 36 may include a specified number of tasks so thatthe total frequency of occurrence is a specific total percentagecoverage value such as 85%, 90% or any other appropriate percentage ofcoverage. Either embodiment typically allows for between fifteen andtwenty tasks to be included in the customer-centric interface.

For efficient operation, the customer-centric interface does not includean opening customer-centric menu prompt listing all of the includedtasks in frequency order. Such an opening menu prompt would take toolong for the customers to listen to and would not allow for quick andeasy navigation of the customer-centric interface. Therefore, thecustomer-centric interface is of a hierarchical design with the tasksgrouped together by task relationships.

In order for the customer-centric interface to be organized from thevantage of the customers, the included tasks need to be groupedaccording to how the customers perceive the tasks to be related.Therefore at step 62, customer structure engine 38 elicits from one ormore test customers each customer's perceptions as to how the includedtasks relate to each other in order to create interface structure forthe customer-centric interface. Interface structure is how the tasks areplaced within the customer-centric interface and organized and groupedwithin the customer-centric menu prompts. For instance, the interfacestructure of a web page refers to how the pages, objects, menu items,and information is organized relative to each other while the interfacestructure for an IVR system refers to the sequence and grouping of thetasks within the customer-centric menu prompts. The interface structurefor the customer-centric interface needs to allow for the customers tofind information and complete tasks as quickly as possible withoutconfusion.

Customer structure engine 38 uses tasks 108-142 from task frequencytable 100 and performs customer exercises with the customers to elicitcustomer feedback regarding how the customers relate and group togethertasks 108-142. For instance, customer structure engine 38 may require agroup of test customers to group tasks 108-142 into one or more groupsof related tasks. In addition, customer structure engine 38 may alsorequire the test customers to make comparative judgments regarding thesimilarity of two or more of the tasks where the test customers statehow related or unrelated they believe the tasks to be. Furthermore,customer structure engine 38 may require the test customers to rate therelatedness of the tasks on a scale. Customer structure engine 38performs the customer exercises using a test IVR system, a web site, orany other appropriate testing means. In addition to eliciting tasksrelationships, customer structure engine 38 also elicits from the testcustomers general names or headings that can be used to describe thegroups of tasks in the customers own language or terminology.

Once customer structure engine 38 elicits from the test customers howthe customers perceive tasks 108-142 to relate to each other, customerstructure engine 38 aggregates the customer feedback and analyzes thecustomer feedback to determine customer perceived task relationships.The customer perceived task relationships are how the customers perceivethe tasks to be related. Customer structure engine 38 represents thecustomer perceived task relationships in a numerical data matrix ofrelatedness scores that represents collectively the customers' perceivedrelatedness of the included tasks.

At step 64, customer structure engine 38 utilizes the customer perceivedtask relationships and the numerical data matrix and combines theincluded tasks into one or more groups of related tasks. For example,using the customer feedback from the customer exercises, customerstructure engine 38 determines that the customers perceive tasks 114,136, and 140 as related and group one, tasks 128, 130, and 138 asrelated and group two, tasks 108, 110, 112, 116, 120, 122, 124, 126,134, and 142 as related and group three, and tasks 118 and 132 asrelated and group four. To aid in the grouping of the tasks and tobetter enable the company to understand the structure and grouping ofthe tasks, customer structure engine 38 represents the customerperceived task relationships and numerical data matrix in a graphicalform. For instance, customer structure engine 38 may generate a flowchart or indogram illustrating a customer-centric call flow for thegroups of tasks.

At step 66, task frequency engine 36 orders the groups of task and thetasks within each group based on the task frequency of occurrence. Taskfrequency engine 36 determines a frequency of occurrence for each groupof tasks by summing the individual frequency of occurrences for eachtask within each group. From the example above, group one has a groupfrequency of occurrence of 8.9% (6.7%+1.1%+1.1%), group two has a groupfrequency of occurrence of 6.2% (3%+2.1%+1.1%), group three has a groupfrequency of occurrence of 59.4%(14%+11.6%+11.3%+5.6%+3.8%+3.8%+3.5%+3.4%+1.4%+1.0%), and group four hasgroup frequency of occurrence of 5.7% (3.8%+1.9%). Task frequency engine36 orders the groups within customer-centric interface in descendingfrequency order so that the tasks having the highest frequency ofoccurrence are heard first by the customers when the customers listen tothe customer-centric menu prompts within the customer-centric interface.Since 59.4% of the customer will be calling about a task in group three,task frequency engine 36 orders group three first followed by group one,group two, and group four.

In addition to ordering the groups of tasks, task frequency engine 36also orders the tasks within each group. Task frequency engine 36 ordersthe tasks within each group according to each task's frequency ofoccurrence from the highest frequency of occurrence to the lowestfrequency of occurrence. For instance, the tasks in group one areordered as task 114, task 136, and task 140. The tasks in group two areordered as task 128, task 130, and task 138. The tasks in group threeare ordered as task 108, task 110, task 112, task 116, task 120, task122, task 124, task 126, task 134, and task 142. The tasks in group fourare ordered as task 118 and task 132. The grouping and ordering of thetasks allow for the high frequency tasks to be more accessible to thecustomers than the low frequency tasks by placing the tasks havinghigher frequency of occurrences higher or earlier in thecustomer-centric interface menu prompts.

At step 68, customer language engine 34, task frequency engine 36, andcustomer structure engine 38 work together to create and order thecustomer-centric menu prompts for the customer-centric interface. Taskfrequency engine 36 and customer structure engine 38 do not take intoaccount customer terminology when calculating task frequencies, groupingthe tasks, and ordering the tasks. So once task frequency engine 36 andcustomer structure engine 38 create interface structure includingordering the included tasks, customer language engine 34 createscustomer-centric menu prompts using the customers own terminology.Customer-centric menu prompts in the language of the customers allow forthe customers to more easily recognize what each menu prompt is askingand allows the customer to accomplish their tasks quickly and withlittle frustration. In alternate embodiments, customer language engine34 may create customer-centric menu prompts using action specific objectwords in addition to the customers own terminology. The use of actionspecific object words to create menu prompts is described in furtherdetail below with respect to FIG. 5.

Once customer-centric interface system 10 creates the customer-centricmenu prompts and the customer-centric interface, customer performanceengine 40 tests the customer-centric interface at step 70 by performingusability tests. Customer performance engine 40 performs the usabilitytests in order to locate and fix any problems with the customer-centricinterface before the customer-centric interface is implemented for useby all customers. The usability tests involve laboratory tests wheretest customers are asked to accomplish sets of tasks using thecustomer-centric interface such as “Call Telephone Company at 555-1111and change your billing address.” In these tests, the test customers usetelephones to interact with the customer-centric interface. Thecustomer-centric interface plays the prerecorded customer-centric menuprompts to the test customers and customer performance engine 40 recordsinformation regarding the test customers' responses such as the menuname for the menus accessed, the amount of time the prerecorded menuprompt played before the test customer made a selection or pressed akey, and the key that the test customer pressed.

When the usability tests conclude, at step 72 customer performanceengine 40 analyzes the results of the usability tests. With respect tothe results, customer performance engine 40 focuses on three differentusability test results: customer satisfaction, task accomplishment, andresponse times. Customer satisfaction is whether or not the testcustomer was satisfied using the customer-centric interface. Customerperformance engine 40 gathers customer satisfaction by asking the testcustomers a variety of questions regarding their experiences ininteracting with the customer-centric interface such as how satisfiedthe test customer was in accomplishing the assigned tasks, how confidentthe test customer was about being correctly routed, the level ofagreement between the selected menu prompts and test customers' assignedtasks, and whether the test customers would want to user thecustomer-centric interface again.

Customer performance engine 40 also determines a task accomplishment orcall routing accuracy score. Task accomplishment measures whether a testcustomer successfully completes an assigned task and is based on asequence of key presses necessary to navigate the customer-centricinterface and accomplish the task. Customer performance engine 40determines if the test customers actually accomplished their assignedtask. For example, if a test customer was assigned the task of using thecustomer-centric interface to inquire about their bill, did the testcustomer correctly navigate the customer-centric menu prompts in orderto inquire about their bill. Customer performance engine 40 examines allthe different menu prompts accessed by the test customers and comparesthe test customer key sequences with the correct key sequences in orderto determine if the test customers accomplished the assigned tasks.

In addition to customer satisfaction and task accomplishment, customerperformance engine 40 also calculates a response time or cumulativeresponse time (CRT) for each customer-centric menu prompt accessed bythe test customers. The response time indicates the amount of time atest customer spends interacting with each customer-centric menu promptand the customer-centric interface. The response times reflects theamount of time the test customers listen to a menu prompt versus theamount of time it takes for the menu prompt to play in its entirety. Theamount of time the test customers spend listening to the menu prompt isnot a very valuable number unless menu duration times are also takeninto account. A menu duration time is the amount of time it takes for amenu prompt to play in its entirety. For instance, a menu prompt mayhave five different options to choose from and the menu duration time isthe amount of time it takes for the menu prompt to play through all fiveoptions.

Customer performance engine 40 records a listening time for each testcustomer for each menu prompt. The listening time is the time the testcustomers actually spend listening to a menu prompt before making aselection. Customer performance engine 40 also has access to the menuduration times for all of the customer-centric menu prompts in thecustomer-centric interface. Customer performance engine 40 calculates aresponse for a menu prompt which is the difference between the listeningtime and the menu duration time by subtracting the menu duration timefrom the listening time.

For example, if the introductory menu prompt of the customer-centricinterface requires 20 second to play in its entirety (menu durationtime) and the test customer listens to the whole menu and then makes aselection, the test customer has a listening time of 20 seconds andreceives a CRT score or response time of 0 (20−20=0). If the testcustomer only listens to part of the menu prompt, hears their choice andchooses an option before the whole menu plays, then the test customerreceives a negative CRT score or response time. For instance, if thetest customer chooses option three 15 seconds (listening time) into thefour-option, 20 second menu prompt, the test customer receives a CRTscore or response time of “−5” (15−20=−5). Conversely, the test customerhas a response time of +15 if the test customer repeats the menu promptafter hearing it once, and then choose option three 15 seconds (35second listening time) into the second playing of the menu (35−20=15).

A negative response time is good because the test customer spent lesstime in the customer-centric interface than they could have and apositive response time is bad because the test customer spent more timethan they should have in the customer-centric interface. In addition tocalculating response times for individual menu prompts, customerperformance engine 40 may also calculate response times for entire tasksand each test customer by summing the menu duration times and thelistening times for each menu prompt required to accomplish the task andsubtracting the total menu duration time from the total listening time.

Once customer performance engine 40 has determined customersatisfaction, task accomplishment, and response times, customerperformance engine 40 generates a performance matrix which chartscustomer satisfaction, task accomplishment, and response times for eachtest customer, each customer-centric menu prompt, and each task. Theperformance matrix allows for customer performance engine 40 todetermine if any of the customer-centric menu prompts or tasks haveunsatisfactory performance at step 74 by examining the combination ofcustomer satisfaction, task accomplishment, and response times andthereby evaluating how well the customer-centric interface performs.Ideally a customer-centric menu prompt and task have a high level ofcustomer satisfaction, a negative or zero response time, and a high rateof task accomplishment. For unsatisfactory performance, customerperformance engine 40 looks for low customer satisfaction, low taskcompletion, or a high positive response time. By charting the customersatisfaction, task accomplishment, and response times on the performancematrix, customer performance engine 40 can determine when one of thetest results is not satisfactory.

If a customer-centric menu prompt or task has unsatisfactory performanceat step 74, then at step 76 customer performance engine 40 selects themenu prompt or task, at step 78 determines the reason for theunsatisfactory performance, and at step 80 modifies the customer-centricmenu prompt or task to correct for the unsatisfactory performance. Forexample, a task may have a high level of customer satisfaction and highrate of task accomplishment but a positive response time. The testcustomers are accomplishing the task and are satisfied when interactingwith the customer-centric interface but are spending too much timeinteracting with the customer-centric interface as indicated by thepositive response time. The positive response time is not good for thecustomer-centric interface because the customers are using unnecessaryresources from the customer-centric interface in the form of too muchtime in accomplishing the task. By examining the menu prompts for thetask, customer performance engine 40 determines that the terminologyused in the menu prompts for the task is not the terminology used by thecustomers. Therefore, customer performance engine 40 alerts customerlanguage engine 34 to the terminology problem and customer languageengine 34 rewords the menu prompts for the task using the customers ownterminology.

Once customer performance engine 40 locates and corrects the problem,customer performance engine 40 determines if there are additional menuprompts or tasks that have unsatisfactory performance at step 82. If atstep 82 there are additional menu prompts or tasks having unsatisfactoryperformance, then at step 84 customer performance engine 40 selects thenext menu prompt or task having unsatisfactory performance and returnsto step 78. Customer performance engine 40 repeats steps 78, 80, 82, and84 until there are no additional menu prompts or tasks at step 82 havingunsatisfactory performance. When there are no additional menu prompts ortasks having unsatisfactory performance at step 82, the process returnsto step 70 and customer performance engine 40 tests the customer-centricinterface having the modified menu prompts or tasks. Customerperformance engine 40 repeats steps 70, 72, 74, 76, 78, 80, 82, and 84until there are no customer-centric menu prompts or tasks havingunsatisfactory performance at step 74.

When there are no customer-centric menu prompts or tasks havingunsatisfactory performance at step 74, at step 86 customer-centricinterface system 10 implements the customer-centric interface for use bythe customers. As customers use the customer-centric interface,customer-centric interface system 10 and customer performance engine 40continually monitor the performance of the customer-centric interfacechecking for low customer satisfaction levels, low task completionrates, or high positive response times at step 88. When customer-centricinterface system 10 discovers an unsatisfactory post-implementationresult such as those described above, customer-centric interface system10 determines the cause of the problem and modifies the customer-centricinterface to correct the problem. As long as the customer-centricinterface is accessible by the customers, customer-centric interfacesystem 10 monitors the customer-centric interface performance andmodifies the customer-centric interface to allow for customer-centricmenu prompts that are worded in the terminology of the customers, thatdirectly match the tasks that the customers are trying to accomplish,and that are ordered and grouped by customer task frequencies and thecustomers' perceptions of task relationships.

FIG. 4 illustrates a block flow diagram of how collection engine 32,customer language engine 34, task frequency engine 36, customerstructure engine 38, and customer performance engine 40 ofcustomer-centric interface system 10 interact and interoperate toautomatically create the customer-centric interface. In addition, FIG. 4also represents the various functions for collection engine 32, customerlanguage engine 34, task frequency engine 36, customer structure engine38, and customer performance engine 40.

Collection engine 32 gathers customer intention information from thecustomer opening statements and includes customer task model 150 whichincludes the list of tasks for which the customers access and use thecustomer-centric interface. Customer language engine 34, task frequencyengine 36, and customer structure engine 38 perform their variousfunctions by processing and manipulating the customer intentioninformation and task list.

Customer language engine 34 develops customer-centric menu prompts forthe customer-centric interface using the customers own terminology.Customer language engine 34 analyzes-the customers' language byanalyzing and tracking every word used by the customers in the customeropening statements to get a feel for how the customers refer to each ofthe tasks. Customer language engine 34 counts each word in each customeropening statement to determine which words the customers use the mostand thereby recognize which of the customers' words are best to use increating customer-centric menu prompts using the customers ownterminology.

In addition to creating customer-centric menu prompts using thecustomers own terminology, in alternate embodiments of customer-centricinterface system 10 customer language engine 34 may also createcustomer-centric menu prompts using action specific object words takenfrom the customer opening statements.

FIG. 5 illustrates a flow diagram for creating customer-centric menuprompts utilizing action specific object words. Customer wordings oftasks in customer opening statements are generally in four differentstyles: action-object (“I need to order CALLNOTES”), action (“I need tomake changes”), object (“I don't understand my bill”), and general (“Ihave some questions”). Menu prompts are typically worded in one of fourstyles: action specific object (“To order CALLNOTES press one”),specific object (For CALLNOTES press two”), general object (“To order aservice press three”), and action general object (“For all otherquestions press four”).

The style of the menu prompt wording can have an effect on theperformance of the menu prompt due to the customers interaction with themenu prompt. Wording menu prompts as action specific object is typicallythe best way to word customer-centric menu prompts because upon hearinga an action specific object menu prompt, the customer generally knowsthat it is the menu prompt they want to select and therefore responsetimes decrease because customers do not have to repeat the menu promptsin order to make a selection. For example, if a customer calls wantingto order CALLNOTES and the second option in the six option menu promptis “To order CALLNOTES press two” then the customer will typically presstwo without listening to the rest of the menu prompts and therefore havea negative response time, high customer satisfaction, and high taskaccomplishment rate.

In order to create customer-centric menu prompts using action specificobject words, customer language engine 34 determines the action wordsand object words used by the customers. At step 152, customer languageengine 34 analyzes the customer opening statements in customer openingstatement database 20 in order to identify the action words and theobject words used by the customers in their opening statements. Inaddition to identifying the action words and the object words, customerlanguage engine 34 also determines which of the action words arespecific action words and which of the object words are specific objectwords. For instance, “order” and “pay” are specific action words and“CALLNOTES” and “Call Waiting” are specific object words while “service”and “question” are not specific object words.

At step 154, customer language engine 34 saves the specific action wordsin specific action database 22 and the specific object words in specificobject database 24. When saving the specific action words and thespecific object words, customer language engine 34 identifies andmaintains the relationships between the specific action words and thespecific object words by linking the specific action words with thespecific object words that were used together by the customers as shownby arrows 156 in FIG. 5. For example, for the customer openingstatements of “I want to buy CALLNOTES” and “I want to inquire about mybill,” “buy” and “inquire” are the specific action words and “CALLNOTES”and “bill” are the specific object words. When customer language engine34 saves the respective specific action words and specific object wordsin databases 22 and 24, a link will be maintained between “buy” and“CALLNOTES” and between “inquire” and “bill.” Maintaining how thecustomers use the action words and object words in databases 22 and 24prevents erroneous combinations of specific action words and specificobject words when creating customer-centric menu prompts. An exampleerroneously combined menu prompt is “To buy a bill press one” since thestatement would not make sense to the customer. The linking of thespecific action words with the specific object words which the customerused together allows for the formation of correct customer-centric menuprompts that make sense to the customers.

In addition to storing the specific action words and the specific objectwords in databases 22 and 24, customer language engine 34 alsocalculates a frequency of occurrence for each specific action word andeach specific object word and stores the specific action words and thespecific object words in databases 22 and 24 in accordance with thefrequency of occurrence in descending frequency order. Therefore, thespecific action words having the highest frequency of occurrence arestored at the top of specific action database 22 and the specific objectwords having the highest frequency of occurrence are stored at the topof specific object database 24.

Once customer language engine 34 determines the frequency of occurrenceand stores the specific action words and the specific object words, atstep 158 customer language engine 34 generalizes the specific actionwords into general groups of specific action words and generalizes thespecific object words into general groups of specific object words.Customer language engine 34 examines the specific action words and thespecific object words for commonalties and then groups the specificaction words and the specific object words together in groups based onthe commonalties. For example, the specific action words of “buy,”“order,” and “purchase” all share the commonality of acquiring somethingand may be grouped together. The specific object words of “CALLNOTES”and “Call Waiting” share the commonality of being residential telephoneservices and therefore may be grouped together. Customer language engine34 assigns names for each of the general groups of specific action wordsand the specific object words and saves the general action words ingeneral action database 26 and the general object words in generalobject database 28 at step 160.

Having specific action database 22, specific object database 24, generalaction database 26, and general object database 28 allows for a greatresource for customer language engine 34 to locate customer terminologywhen creating customer-centric menu prompts. For creating upper levelhierarchical menu prompts, customer language engine 34 uses words fromgeneral action database 26 and general object database 28. To createaction specific object menu prompts in the words of the customers forlower level hierarchical menu prompts, customer language engine 34 useswords from specific action database 22 and specific object database 24.Because the specific action words and the specific object words areordered by frequency in databases 22 and 24, customer language engine 34can create action specific object menu prompts using the customerterminology most often used by the customers.

While customer language engine 34 determines the customer terminologyand wording to use for the customer-centric menu prompts, task frequencyengine 36 determines the frequency of occurrence for the tasks that thecustomers call about and also determines which tasks will be included inthe customer-centric interface. Generally the customer openingstatements are from more than one call center so when determining thefrequency of occurrence for each task, task frequency engine 36 takesinto account the volume of calls into each call center when constructingthe task frequency table so that the frequency results are accurate.Frequency of occurrence data must be weighted so that a call centerreceiving three million calls does not have the same weight as a callcenter receiving ten million calls.

Once task frequency engine 36 determines the tasks to be included in thecustomer-centric interface including all tasks down to 1% frequency orto a percentage coverage, customer structure engine 38 elicits customerperceived task relationships for the included tasks as described above.Utilizing the customer perceived task relationships, customer structureengine 38 creates interface structure for the customer-centric interfaceand represents the interface structure both as a numerical data matrixand a graphical representation.

At box 162, customer language engine 34, task frequency engine 36, andcustomer structure engine 38 work together to automatically create thecustomer-centric interface. Customer language engine 34 contributes thewording of the customer-centric menu prompts in the customers ownterminology for the customer-centric interface. Task frequency engine 36provides the tasks that are to be included in the customer-centricinterface, the ordering of the groups of tasks in the menu prompts, andthe ordering of the tasks within the groups of tasks. Customer structureengine 38 provides the interface structure or grouping of tasks for thecustomer-centric interface. After the automated creation of thecustomer-centric interface, customer performance engine 40 performsusability tests on the customer-centric interface as described above andevaluates and reconfigures the customer-centric interface based oncustomer satisfaction, task accomplishment, and response times duringboth the testing phase and implementation.

Customer-centric interface system 10 allows for the automated creationof a customer-centric interface that directly matches menu prompts withcustomer tasks, orders and groups the tasks and menu options by the taskfrequency of occurrence and the customer perceived task relationships,and states the menu prompts using the customers own terminology.Although the present invention has been described in detail with respectto an IVR system, customer-centric interface system 10 may also beutilized for the automated creation of customer-centric interfaces forweb sites with respect to developing content for the web site, designsof the web pages, and what tasks to locate on different web pages.

Although the present invention has been described in detail, it shouldbe understood that various changes, substitutions and alterations can bemade hereto without the parting from the spirit and scope of theinvention as defined by the appended claims.

1. A method for providing an interface, comprising: creating a pluralityof prompts for the interface, each of the plurality of promptsrepresenting tasks to be accomplished by a user, and each of theplurality of prompts being obtained based on user input; grouping theplurality of prompts according to relationships, obtained from the userinput, among the tasks; and updating the interface based on userfeedback, wherein each of the plurality of prompts are designated usinguser terminology obtained from the user input.
 2. The method accordingto claim 1, wherein the interface is graphically represented.
 3. Themethod according to claim 1, wherein the interface comprises aninteractive voice system.
 4. The method according to claim 1, whereinthe interface determines a structure of a website.
 5. The methodaccording to claim 1, further comprising: developing content for awebsite based on the interface.
 6. The method according to claim 1,further comprising: ordering the plurality of prompts according tofrequencies of occurrence for tasks corresponding to each of theplurality of prompts.
 7. A system implemented on at least one processorfor providing an interface, comprising: a receiver operable to receiveuser input; and a rendering processor operable to create a plurality ofprompts for the interface, each of the plurality of prompts representingtasks to be accomplished by a user, and each of the plurality of promptsbeing obtained based on the user input; a grouping processor operable togroup the plurality of prompts according to relationships, obtained fromthe user input, among the tasks; and an updating processor operable toupdate the interface based on user feedback, wherein each of theplurality of prompts are designated using user terminology obtained fromthe user input. 8 . The system according to claim 7, wherein the taskscomprise action data that describes an action the user would like toperform with respect to an object.
 9. The system according to claim 8,wherein the tasks comprise object data that describes the object uponwhich the action is performed.
 10. The system according to claim 9,wherein the object data comprises user-specific data stored by thesystem.
 11. The system according to claim 8, wherein the action datacomprises data relating to at least one of accessing and interactingwith the object.
 12. The system according to claim 9, furthercomprising: a task generating processor operable to create asystem-generated task by combining object data obtained from a firsttask with action data obtained from a second task.
 13. The systemaccording to claim 7, wherein the user input is not based onpredetermined input.
 14. The system according to claim 7, wherein theuser input is obtained from at least one of a telephone call, anelectronic mail, and a submission via a website.
 15. The systemaccording to claim 7, wherein the interface is modified based on theuser feedback obtained from monitoring the user interacting with theinterface.
 16. A computer readable medium storing a computer program,recorded on the computer readable medium, for providing an interface,the medium comprising: a creation code, recorded on the computerreadable medium, executable to create a plurality of prompts for theinterface, each of the plurality of prompts representing tasks to beaccomplished by a user, and each of the plurality of prompts beingobtained based on user input; a grouping code, recorded on the computerreadable medium, executable to group the plurality of prompts accordingto relationships, obtained from the user input, among the tasks; and anupdating code, recorded on the computer readable medium, executable toupdate the interface based on user feedback, wherein each of theplurality of prompts are designated using user terminology obtained fromthe user input.
 17. The computer readable medium according to claim 16,further comprising: a determining code, recorded on the computerreadable medium, executable to determine user terminology based on afrequency of occurrence for each word obtained from the user input. 18.The computer readable medium according to claim 16, wherein theplurality of prompts are modified based on an accuracy score foraccomplishing a specified task using the interface.
 19. The computerreadable medium according to claim 18, further comprising: areconfiguring code, recorded on the computer readable medium, operableto reconfigure the interface based on response times for accomplishingthe specified task using the interface.
 20. The computer readable mediumaccording to claim 16, wherein the interface comprises an interface to acomputing device.